Slop, Scams, and Super PACs

· The Fluency Briefing

The Fluency Briefing

Your Guide to What's Happening in AI and Why It Matters to You

Monday, June 22, 2026

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Sixty percent of TikTok videos served to new users are AI-generated slop. Meanwhile, the electricians building the data centers that power all that slop are getting ghosted on dates for it, and AI-backed Super PACs just dropped $44 million on a single Manhattan congressional race. Whether it's your feed, your power grid, or your ballot, AI's fingerprints are everywhere this Monday - and the mess is getting personal.

Today in AI:

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Today's Takeaway:

Here's the thread connecting today's biggest stories: AI isn't just a software problem anymore - it's becoming a physical, social, and political infrastructure issue all at once. The Kapwing study reported by 9to5Mac showing 59% AI slop on TikTok is a first-order problem. Second order: platforms that reward upload volume over quality will keep incentivizing this flood, which means human creators get buried. Third order: the platforms your business uses for marketing are becoming less trustworthy as distribution channels. If your content strategy relies on TikTok reach, you're now competing with infinite cheap AI output for increasingly skeptical eyeballs.

Meanwhile, the physical side of AI expansion is creating friction that doesn't show up in funding announcements. Wired's reporting on electricians facing social stigma for data center work reveals something investors rarely discuss: the human cost of building AI's backbone goes beyond wages. Communities are pushing back against data centers, and the workers building them are caught in the middle. When Meta launches trade academies and Google commits $50 million to training programs, they're not just being generous - they're trying to solve a labor pipeline problem that social backlash is making worse. The AI boom needs physical hands, and those hands are starting to have opinions about what they're building.

🔍 Myth Buster

The myth: "Bigger AI models are always smarter and better"

The reality: GLM-5.2, a newer open model, is now outperforming many larger closed models on benchmarks - demonstrating that architectural improvements and training efficiency can beat raw scale. Researchers have repeatedly shown that smaller, well-optimized models can match or exceed models many times their size; for example, Meta's Llama 3 8B outperformed GPT-3.5 on several benchmarks despite being a fraction of the size. The AI field is increasingly shifting toward 'better, not bigger' as the dominant design philosophy.

The nuance: Model size does still correlate with capability ceilings in certain complex reasoning and multi-step tasks, so scale isn't irrelevant - it just isn't the only axis that matters anymore.

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The Bottom Line

The Pattern: AI is no longer contained in apps and APIs. It's reshaping physical labor markets, flooding content platforms to the point of uselessness, and writing checks large enough to buy congressional races. The infrastructure, the content, and the politics are all moving at the same breakneck speed.

Why It Matters: If you run a business, this hits you from three directions at once. Your marketing channels are drowning in AI noise, your costs for AI-dependent services are rising as infrastructure battles intensify, and the regulatory environment is being actively shaped by people spending $100 million to influence it. Ignoring any one of these is a strategic blind spot.

Your Move: Audit your TikTok and YouTube content performance from the last 90 days. If engagement is dropping despite consistent output, you're probably losing ground to the slop flood. This Monday is a good day to ask whether your distribution strategy still makes sense in a world where 60% of what surrounds your content was made by a machine.

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